Sensitivity of the Empirical Mode Decomposition to Interpolation Methodology and Data Non-stationarity-Reference-Cited by-同舟云学术

Sensitivity of the Empirical Mode Decomposition to Interpolation Methodology and Data Non-stationarity

Published:2019-02-12 Issue:4 Volume:24 Page:437-456
ISSN:1420-2026
Container-title:Environmental Modeling & Assessment
language:en
Short-container-title:Environ Model Assess

Author:

Z. Bahri F. M.^ORCID,Sharples J. J.

Funder

University of New South Wales Canberra

Australian Research Council

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Link

http://link.springer.com/content/pdf/10.1007/s10666-019-9654-6.pdf

Reference35 articles.

1. Bahri, F.M., & Sharples, J.J. (2015). Sensitivity of the Hilbert-Huang transform to interpolation methodology: examples using synthetic and ocean data. In MODSIM2015, 21st international congress on modelling and simulation. Modelling and simulation society of Australia and New Zealand (pp. 1324–1330).

2. Chen, Q, Huang, N, Riemenschneider, S, Xu, YA. (2006). B-spline approach for empirical mode decompositions. Advances in Computational Mathematics, 24, 171–195.

3. Dätig, M, & Schlurmann, T. (2004). Performance and limitations of the Hilbert–Huang transformation (HHT) with an application to irregular water waves. Ocean Engineering, 31, 1783–1834.

4. Deering, R, & Kaiser, JF. (2005). The use of a masking signal to improve empirical mode decomposition. In IEEE international conference, acoustics, speech, and signal processing, 2005. Proceedings. (ICASSP’05) (Vol. 4, p. iv–485).

5. Donnelly, D. (2006). The fast Fourier and Hilbert-Huang transforms: a comparison. Computational Engineering in Systems Applications, 1, 84–88.

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